Theoretical Bounded-Agent Security: Guardrails for Verifiable and Validated Government Intelligence Reporting
A formal framework combining theoretical computer science with practical AI safety requirements for public-sector intelligence reporting systems — presented at the 40th AAAI Conference on Artificial Intelligence.
Authors
Abstract
We introduce a formal framework for bounded-agent security guardrails that ensures verifiable and validated outputs from AI-assisted government intelligence reporting systems. The framework defines mathematical boundaries on agent behavior through formal verification constraints, provenance tracking requirements, and adversarial testing protocols. We demonstrate that bounded-agent architectures can provide provable safety guarantees while maintaining practical utility for real-world government intelligence workflows. Our approach combines insights from formal methods, information flow control, and adversarial machine learning to create guardrails that are both theoretically sound and operationally feasible.
The Guardrails Framework
Our bounded-agent security framework rests on four interconnected guardrail layers, each providing specific formal guarantees:
1 Formal Verification Boundaries
Mathematical constraints on agent reasoning scope, ensuring that AI-generated intelligence outputs remain within predefined logical and factual boundaries. Implemented through type-theoretic specifications and runtime assertion checking.
2 Provenance & Audit Trails
Every intelligence output is accompanied by a complete provenance graph tracing each inference step to its source data and reasoning rule. Enables full auditability and replay verification by human analysts.
3 Adversarial Robustness Testing
Systematic red-team evaluation protocols that probe guardrail boundaries under adversarial conditions, including prompt injection, data poisoning, and specification gaming scenarios specific to government reporting contexts.
4 Human-in-the-Loop Validation
Structured interfaces for human analysts to review, override, or certify AI-generated outputs, with formal accountability handoffs that preserve the bounded-agent guarantee even during human intervention.
Real-World Application: This framework directly applies to HUD data analytics and government program effectiveness reporting. Our guardrails ensure that AI-assisted analysis of housing data, demographic trends, and policy outcomes meets evidentiary standards for public-sector decision-making.
Related Research Context
Housing Policy Research Grant
This AAAI research directly informs our approach to HUD NOFO PDR-2600-DC-029M, applying bounded-agent security principles to ensure verifiable housing policy analytics.
View NOFO DetailsCommunity Data Analytics
Guardrail-verified analytics pipelines for housing market intelligence, foreclosure tracking, and demographic analysis across Central Texas communities.
Community DataSection 3 Compliance
Applying formal verification methods to Section 3 compliance tracking and economic opportunity reporting for HUD-funded projects.
Section 3 ServicesHUD NOFO PDR-2600-DC-029M — Application Context
Our AAAI research framework is being applied to the HUD Housing Policy Research Grant (NOFO PDR-2600-DC-029M), due June 1, 2026. This application demonstrates how bounded-agent security guardrails enable verifiable government intelligence reporting at scale.
| Field | Value |
|---|---|
| Status | Open |
| Total Funding | $8,000,000 |
| Award Ceiling | $1,500,000 |
| Deadline | June 1, 2026 — 11:59:59 PM ET |
| Performance Period | 12 – 30 months |
| Cloud Fronts Eligibility | YES — For-profit / small business eligible |
Research Topics We Address
Government-Induced Demand & Housing Affordability
Applying bounded-agent analytics to model the effects of federal housing policy on local market dynamics.
Alternative Local Government Financing Models
Analyzing non-property-tax revenue models with guardrail-verified economic impact assessments.
Opportunity Zone Impact Quantification
Formal methods for measuring Opportunity Zone program outcomes with verifiable data provenance.
How to Apply: This NOFO requires a two-phase submission process. View on Grants.gov · Registration on SAM.gov and Grants.gov required.
Note: HUD does not meet individually with applicants. All questions are answered publicly via the FAQ document. Debriefings available after awards are announced.